Unravelling the Yeast Cell Cycle Using the TriGen Algorithm

Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping allowing genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of temporal microarray data in which the genes are evaluated under certain conditions at several time points. In this paper, we present the results of applying the TriGen algorithm, a genetic algorithm that finds triclusters that take into account the experimental conditions and the time points, to the yeast cell cycle problem, where the goal is to identify all genes whose expression levels are regulated by the cell cycle

Application of phenotypic microarrays to environmental microbiology

Environmental organisms are extremely diverse and only a small fraction has been successfully cultured in the laboratory. Culture in micro wells provides a method for rapid screening of a wide variety of growth conditions and commercially available plates contain a large number of substrates, nutrient sources, and inhibitors, which can provide an assessment of the phenotype of an organism. This review describes applications of phenotype arrays to anaerobic and thermophilic microorganisms, use of the plates in stress response studies, in development of culture media for newly discovered strains, and for assessment of phenotype of environmental communities. Also discussed are considerations and challenges in data interpretation and visualization, including data normalization, statistics, and curve fitting

A Randomized Real-Valued Negative Selection Algorithm

This paper presents a real-valued negative selection algorithm with good mathematical foundation that solves some of the drawbacks of our previous approach [11]. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. The proposed method is a randomized algorithm based on Monte Carlo methods. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance. © Springer-Verlag Berlin Heidelberg 2003

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

In the Abstract, the url https://modelseed.org has been replaced with https://modelseed.org/biochem

Validating Gene Clusterings by Selecting Informative Gene Ontology Terms with Mutual Information

We propose a method for global validation of gene clusterings. The method selects a set of informative and non-redundant GO terms through an exploration of the Gene Ontology structure guided by mutual information. Our approach yields a global assessment of the clustering quality, and a higher level interpretation for the clusters, as it relates GO terms with specific clusters. We show that in two gene expression data sets our method offers an improvement over previous approaches

Antioxidant pathways are up-regulated during biological nitrogen fixation to prevent ROS-induced nitrogenase inhibition in Gluconacetobacter diazotrophicus

Gluconacetobacter diazotrophicus, an endophyte isolated from sugarcane, is a strict aerobe that fixates N2. This process is catalyzed by nitrogenase and requires copious amounts of ATP. Nitrogenase activity is extremely sensitive to inhibition by oxygen and reactive oxygen species (ROS). However, the elevated oxidative metabolic rates required to sustain biological nitrogen fixation (BNF) may favor an increased production of ROS. Here, we explored this paradox and observed that ROS levels are, in fact, decreased in nitrogen-fixing cells due to the up-regulation of transcript levels of six ROS-detoxifying genes. A cluster analyses based on common expression patterns revealed the existence of a stable cluster with 99.8% similarity made up of the genes encoding the α-subunit of nitrogenase Mo–Fe protein (nifD), superoxide dismutase (sodA) and catalase type E (katE). Finally, nitrogenase activity was inhibited in a dose-dependent manner by paraquat, a redox cycler that increases cellular ROS levels. Our data revealed that ROS can strongly inhibit nitrogenase activity, and G. diazotrophicus alters its redox metabolism during BNF by increasing antioxidant transcript levels resulting in a lower ROS generation. We suggest that careful controlled ROS production during this critical phase is an adaptive mechanism to allow nitrogen fixation